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Turning Soils into Sponges How Farmers Can Fight Floods and Droughtswww.ucsusa.org/SoilsIntoSponges
Appendix A: Methods and Experiments Included in the Infiltration
Rate Meta-Analysis
Appendix B: Methods and Experiments Included in the Porosity and
Field Capacity Meta-Analysis
Appendix C: Methods for the Hydrology Modeling Analysis
References
© August 2017
All rights reserved
Appendix A: Methods and Experiments Included in the Infiltration
Rate Meta-AnalysisRationale for Practice Selection
In this analysis, we focused on the principles of conservation agriculture as outlined in prior reviews and meta-analyses (Powlson et
al. 2016; Pittlekow et al. 2015; Palm et al. 2014) which typically include: zero tillage practices that eliminate conventional tillage and
associated soil disturbance (referred to as no-till); cover cropping or green manure practices that keep soils covered as compared to leaving
them bare (cover crops); and diversified farming practices (including crop rotations and intercropping) as compared to monoculture cropping
(crop rotations).We also assessed the impact of additional agricultural practices based on ecological principles, primarily perennially
managed systems (including agroforestry, perennial grasses, and managed forestry), compared to annual cropping practices only
(perennials). Finally, we looked at the case of cropland grazing (e.g., grazing crop residues or planted pasture grazing), as compared to
conventionally harvested or hayed cultivated fields, to understand how this phase of integrated crop and livestock systems affects infiltration
rates (crop and livestock).
Finally, in order to investigate the potential of different management practices on grass-based grazing systems, we searched for
experiments that evaluated several different livestock grazing practices and measured infiltration rates. These practices included the impact of
increased stocking complexity and reduced stocking rates or densities (grazing management) as well as the impact of strategically excluding
livestock for some period of time (grazing exclusion).
Literature Search
The primary literature search was conducted using EBSCO Discovery ServiceTM, which includes more than 23,000 publications
from databases such as JSTOR and publishers such as Wiley, Elsevier, Springer-Nature, IOP, Royal Society, Oxford, Cambridge, Thomson
Reuters, AAAS, and the American Society of Agronomy. The EBSCO Discovery ServiceTM matches on subject headings, keywords, and
keywords in abstracts. The keyword strings for the crop analysis included “infiltration W1 rate” AND “crop*” for all searches and additional
keywords are described below for each practice. For the grazing experiments, our keyword search included the terms “infiltration W1 rate”
AND graz*”. These keyword terms returned more than 800 possible studies to evaluate, of which 116 ultimately fit our criteria of
experiments that had an appropriate experimental design (descriptions included by category) while also measuring water infiltration.
After the search with EBSCO Discovery ServiceTM was complete, we used the USDA-NRCS Soil Health Literature database to find
additional research papers. This source is compiled by the NRCS Soil Health Division by searching databases such as Google Scholar to find
peer-reviewed publications that categorize the impact of agricultural management on a range of soil properties (NRCS 2016). It is updated
regularly by staff and includes more than 400 peer reviewed references (as of September 2016). The meta-data also note which experiments
include information on infiltration rates. From this search, we added 10 additional studies for a total of 126 included in this analysis.
No-Till Experiments
Papers identified from the additional search term “till*” were included if experiments clearly included a no-till treatment. We did
not compare reduced tillage to conventional tillage (as some no-till meta-analyses have done, e.g., van Kessel et al. 2014). However, when
papers included multiple tillage practices that could have been counted as a control treatment, we included all comparisons in the dataset and
classified them as conventional or reduced tillage based on the reported equipment and/or method of tillage.
1 Adapted from Basche and DeLonge (n.d.) and DeLonge and Basche (n.d.).
Cover Crop Experiments
Papers identified from the additional search string of “cover crop*” OR “green manure” OR “catch crop*” were included when a
control treatment with no cover crop was present (e.g., bare soil when the cash crop was not growing). Experiments were included when the
cover crop was planted and grown intentionally to protect the soil and was not harvested, and residues were mechanically terminated,
chemically terminated, or left as a green manure (e.g., a crop grown specifically for fertility purposes).
Crop Rotation Experiments
Papers identified from the additional search string of “rotation” AND “continuous” were included when there was a control
treatment that represented the continuous cropping of one cash crop. The experimental treatment needed to include the same crop as well as
at least one additional crop, grown in rotation, similar to the protocol utilized by McDaniel et al. (2014). We also included two experiments in
which an additional crop was grown not as a rotation but as an intercrop (i.e., two different plant species grown simultaneously on the same
field) and one experiment that met the crop rotation criteria but also included livestock grazing in the experiment treatment but not the control
(Table 1). In all experiments, we recorded the number of crops included in the treatment cropping system for more detailed analysis.
Perennial Experiments
Papers identified from the additional search string of “perennial” OR “agroforest*” included experiments in which a perennial
treatment was compared to a cultivated annual cropping treatment. In this category, we included experiments with a range of treatments,
including perennial grasses, agroforestry and managed forestry (Table 1). Control treatments were all annual cropping systems, although they
varied slightly by experiment (e.g., they included monocultures either with or without conventional tillage). Two of the eight experiments
included in this category also included livestock grazing in the treatment (with an annual crop system with no livestock as a control; Table 1).
Crop and Livestock Experiments (Cropland Grazing)
Papers identified from the additional search string of “graz*” AND “livestock” were included if there was a crop-only control
treatment (including pasture with cultivated forage crops) and an experimental treatment of similar crop systems with livestock grazing (of
crop residues or forage), representative of one potential phase of integrated crop and livestock systems. This group included experiments with
either annual crop or pasture-based systems, in which control treatments were harvested traditionally (i.e., with equipment) and were not
grazed. These experiments differed from the three other experiments with livestock included in the study (one crop rotation and two perennial
studies) in that the primary treatment in this case was livestock grazing versus traditional harvesting and not a change to a crop rotation or a
switch from annual to perennial crop systems.
Improved Grazing and Livestock Exclusion
Papers identified from the keyword search of “graz*” AND “infiltration W1 rate” were grouped into the following categories:
Increased stocking complexity: Experiments were included in this category if they represented a switch from a continuous (year-
round or seasonal) grazing pattern to a more complex or strategic managed system (Table 2). This primarily included stocking patterns
changing from a continuously grazed system (year-round or seasonal) to systems managed using more complex strategies (e.g., rotational,
mob, adaptive, etc.). We also searched for cases of increasing management complexity through variables, such as by moving from a fully
grass-based system to silvopasture. However, we found only one paper (Sharrow 2007) that met those criteria. Although this category
primarily included comparisons that added complexity while they kept stocking rates (ha AU-1 y-1) very similar, there were three studies that
did include a relatively high change in stocking rate (see Table 2); in two cases the increased complexity was combined with an increase in
stocking rate (i.e., reduction in stocking pressure; Taddesse 2002; one site in Weltz 1986), whereas one case involved a decrease in stocking
rate (Proffitt 1995).
Reduced stocking rates or densities: Treatments were included in this category if they represented a reduction in grazing pressure
without any clear changes to grazing land management complexity (e.g., without switching from continuous to rotational grazing; see Table
3). Changes in grazing rates or densities were reported as a variety of variables or indices (stocking rate, stocking density, residual
phytomass, or degradation/vegetation type).
Grazing exclusion: We found that numerous experiments from our search included treatments in which livestock were strategically
excluded from grazing areas for a specified period. In fact, 58 percent (10/17) of the complexity studies and 88 percent (15/17) of the
stocking rate studies included grazing exclosure measurements (Tables 2 through 4). Additionally, we identified 15 more studies from our
keyword search that had measurements on exclosure, but did not fit into the other two categories. We therefore included this category for
analysis to determine if there was an effect on infiltration rates from intentional livestock exclusion, defining the experimental treatment as
the exclosure and the controls to be the grazed treatments (either continuous or complex). In most cases, grazing was excluded from an area
that was previously grazed. We further categorized the exclosure treatments based on what type of grazing they were being protected from
(complex vs. continuous, and a light, moderate, heavy, or very heavy stocking rate, as defined by the authors). Treatment duration was
defined as the time since the exclosure was introduced; note that this was not always equivalent to the time since introduction of the grazing
pattern that was represented by the control and, therefore, some of the grazing regimes in the controls should be considered only a proxy for
the grazed condition.
Database Design
After experiments were determined to fit the criteria for study inclusion, key data were categorized in a systematic way. Many
experiments reported both initial infiltration rates as well as steady-state infiltration, and to consistently capture treatment effects, our analysis
only included values of steady-state infiltration (i.e., the final infiltration or constant rate, regardless of initial soil moisture conditions (Hillel
1998). We included studies that reported different measures of steady-state infiltration (e.g., the total volume of water infiltrated over a
defined period). When experiments included multiple measurements of infiltration rate in an individual crop season or year, measurements
were averaged. When experiments reported measurements over several years, each value was included separately.
Statistical Analysis
The main statistical analysis was conducted by calculating response ratios, representing a comparison of the experimental to control
treatments, as is common in meta-analysis methodology (Hedges et al. 1999). Response ratios represented the natural log of the infiltration
rate measured in the experimental treatment divided by the infiltration rate measured in the control treatment. A weighting factor was
included in the statistical model as suggested by Philibert et al. (2012) and was created based on the experimental replications of each study
(Adams et al. 1997) for the crop comparisons only. Due to the limited reporting of standard errors or standard deviations, as well as the fact
that many grazing studies do not include true replications (experimental designs frequently included only subsamples from larger areas or
transects, as opposed to a true randomized block design), we performed an unweighted meta-analysis for the grazing experiments (Eldridge et
al. 2016).There were a few studies that represented experimental designs and that took subsamples from larger areas rather than taking
independent samples from true randomized block designs, and for these studies we assigned a replication value of “1,” which would ascribe a
lower weight in the statistical calculations for these experiments (five studies fell into this criteria). Natural log results were back-transformed
to a percent change to ease interpretation of results. Results were considered significant if the 95 percent confidence intervals did not cross
zero.
An additional analysis was conducted to evaluate the absolute change in infiltration rates (as compared to the response ratio) to
demonstrate the magnitude of potential improvement in relation to more intense precipitation events. When possible, values for infiltration
rates were converted to mm hr-1 to evaluate the absolute difference between experimental treatments and control treatments. For this portion
of the analysis, we counted only values where absolute infiltration rates were reported (as compared to a volume of water infiltrated). We
considered a threshold of a one inch per hour (25 mm hr-1) to represent a significant rain event.
For the main statistical analyses, the five different practices were analyzed separately, because there were notable differences in experimental designs
and in control treatments. We looked at the full dataset for more observational comparisons including the overall trends and the absolute change in
infiltration rates. A mixed model (lme4 package in R) was used to calculate category means and standard errors, including a random effect of study to
account for similar study environments when experimental designs allowed for multiple paired observations (e.g., different tillage practices, different
cover crop species) (St. Pierre 2001). Groups were considered to be statistically significant if error bars did not cross zero.
TABLE A.1. Description of Experiments included in the Meta-Analysis Database: Cropping System Comparisons
State/Region, Country Category Main Cropping System
and Description of
Experimental Treatment
Control Treatment Reference
Denmark cover crop, no-till barley with radish cover crop,
no-till
no cover crop, conventional
tillage, reduced tillage
Abdollahi and Munkholm
2014 Texas, USA crop rotation, no-till sorghum-wheat continuous sorghum, reduced
tillage
Alemu, Unger and Jones 1997
Yurimaguas, Peru crop and livestock trees, pasture, maize, and livestock grazing
trees and pasture^ Arevalo et al. 1998
British Columbia, Canada no-till continuous barley conventional tillage Arshad, Franzluebbers and
Azooz 1999 Central Mexico cover crop, no-till no-till, maize with vetch or
oat cover crop
conventional tillage, maize
without a cover crop
Astier et al. 2006
Uttarakhand, India no-till rice-wheat no-till conventional tillage Bajpai and Tripathi 2000 Santa Cruz, Bolivia no-till wheat-soybean-sunflower no-
till
conventional tillage, reduced
tillage
Barber et al. 1996
Texas, USA no-till wheat-sorghum-fallow no-till reduced tillage Baumhardt and Jones 2002 Texas, USA crop rotation wheat-sorghum continuous wheat Baumhardt, Johnson and
Schwartz 2012
Uttar Pradesh, India no-till rice-wheat no-till conventional tillage, reduced tillage
Bazaya et al. 2009
NSW, Australia crop and livestock wheat or canola with sheep
grazing
canola and wheat only Bell et al. 2011
Iowa, USA perennial silver maple, grass filter,
switchgrass, grazed pasture#
maize-soybean* Bharati et al. 2002
Uttarakhand, India no-till rice-wheat no-till conventional tillage Bhattacharyya et al 2008 Kansas, USA crop rotation sorghum-wheat-soybean continuous sorghum Blanco Canqui et al. 2010
Kansas, USA cover crop winter wheat-grain sorghum
with sunnhemp and late maturing soybean cover crops
winter wheat-grain sorghum
with no cover
Blanco Canqui et al. 2011
Georgia, USA no-till sorghum-soybean no-till conventional tillage, reduced
tillage
Bruce et al. 1990
Georgia, USA cover crop and no-till soybean-grain sorghum-
crimson clover no-till~
conventional tillage soybean-
grain sorghum-fallow
Bruce et al. 1992
Southern Malawi perennial maize with sesbania,
gliricidia, leucaena, acacia
intercrops
continuous maize Chirwa, Mafongoya and
Chintu 2003
Oklahoma, USA no-till continuous wheat no-till conventional tillage Dao 1993
Northern Pampean Region,
Argentina
crop and livestock maize-soybean and grass
alfalfa pasture rotation with cattle grazing
maize-soybean only Fernandez, Alvarez and
Taboada 2015
Kampala, Uganda cover crop maize-bean with crotaleria
green manure
maize-bean only Fischler, Wortmann and Feil
1999 California, USA cover crop almond orchard with
bromegrass or clover cover
crop, tomato with oat or vetch cover crop
orchard no cover crop, tomato
no cover crop
Folorunso et al. 1992
Ibadan, Nigeria no-till continuous maize no-till reduced tillage Franzen et al. 1994
Georgia, USA crop and livestock varied intensity cattle grazing on forage grass
hayed forage grass^ Franzluebbers et al. 2012
Georgia, USA no-till sorghum-maize-cereal rye
cover crop no-till, winter wheat-pearl millett cover crop
no-till
conventional tillage Franzluebbers et al. 2008
Meerut, India no-till rice-wheat no-till conventional tillage, reduced tillage
Gangwar et al. 2006
Central Indus Plain, India cover crop rice-wheat-sesbania green
manure
rice-wheat without cover crop Ghafoor et al. 2012
Meghalaya, India perennial perennial grasses cut for
livestock feed
continuous cultivation annual
crops
Ghosh et al. 2009
Southern Nigeria no-till maize-maize-cowpea no-till conventional tillage Ghuman and Lal 1992 Southwest Spain no-till oat-triticale-vetch-brassica
no-till
conventional tillage Gomez-Paccard et al. 2015
Central Mexico crop rotation, no-till maize-wheat (crop rotation), no-till
continuous maize and continuous wheat (crop
rotation)*, conventional
tillage
Govaerts et al. 2007
Erzurum, Turkey no-till wheat-vetch no till conventional tillage, reduced
tillage
Gozubuyuk et al. 2014
California, USA cover crop grape vineyard with bromegrass cover crop
grape vineyard no cover crop Gulick et al. 1994
Dodoma, Tanzania no-till sorghum no till conventional tillage, reduced tillage
Guzha 2004
Shaanxi Province, China no-till winter wheat no-till (with
residue retention)~
conventional tillage He et al. 2009
Uttar Pradesh, India no-till rice-wheat no till conventional tillage Jat et al. 2009
Uttar Pradesh, India no-till maize-wheat no till conventional tillage Jat et al. 2013
Punjab Province, Pakistan cover crop wheat-cotton with a jantar green manure
no cover crop Kahlown and Azam 2003
Iowa, USA cover crop maize-soybean-winter rye
cover crop
maize-soybean no cover crop Kaspar, Radke and Laflen
2001 Ibadan, Nigeria no-till maize-cowpea-soybean no-till conventional tillage Kayombo et al. 1991
Southern Ethiopia perennial maize, forestry, and cattle
grazing#
continuous maize with tillage Ketema and Yimer 2014
West Bengal, India no-till peanut no-till conventional tillage, reduced
tillage
Khan 1984
Ohio, USA crop rotation, no-till maize-soybean, no-till continuous maize, reduced tillage
Kumar et al. 2012
Meghalaya, India no-till groundnut-rapeseed no-till conventional tillage Kuotsu et al. 2014
South-Limbourg, Netherlands cover crop maize silage with winter rye or summer barley cover crops
no cover crop Kwaad and Van Milligan 1991
Ibadan, Nigeria cover crop maize-cowpea-pigeon pea-
cassava-soybean with cover
crops
no cover crop Lal et al. 1978
Ibadan, Nigeria no-till continuous maize moldboard plow, ridge till, Lal 1997
disc plow
Ohio, USA no-till maize-soybean no-till reduced tillage Lal et al. 1989 Rajasthan, India no-till sorghum interseeded with
green gram
conventional tillage, reduced
tillage
Laddha and Totawat 1997
Georgia, USA perennial long leaf pine, planted pine corn-soybean conventional
tillage
Levi et al. 2010
North Dakota, USA perennial, no-till grazed pasture (perennial),
spring wheat-winter wheat no-till (no-till)~
annual cropping sequence
with no grazing (perennial), conventional tillage with
spring wheat-fallow (no-till)
Liebig et al. 2004
North Dakota, USA crop and livestock, perennial oat/pea-triticale/sweet clover-maize no till with grazing
animals (crop and livestock),
western wheatgrass pasture cut for forage (perennial)
hayed pastured grass (crop and livestock)*^, oat/pea-
triticale/sweet clover-maize
no till with grazing animals (perennial)
Liebig et al. 2011
Pulawy, Poland no-till maize-spring barley-winter
rape-winter wheat-faba bean no-till
conventional tillage, reduced
tillage
Lipiec 2006
Mississippi, USA no-till, cover crop cotton-soybean no-till with
rye or vetch cover crop
no cover crop, reduced tillage Locke et al. 2012
Iowa, USA no-till maize-soybean no-till conventional tillage, reduced
tillage
Logsdon et al. 1992
Punjab Province, Pakistan cover crop cotton-wheat with berseem grown as a green manure
cotton-wheat no cover crop Mahmood-ul-Hassan, Rafique and Rashid 2013
Tel Hadya, Syria crop and livestock wheat-lentil-chickpea-vetch-
watermelon with livestock
crops only no grazing Masri and Ryan 2006
Georgia, USA cover crop grain sorghum with vetch or
wheat cover crop
sorghum fallow no cover crop McVay et al. 1989
New York, USA no-till maize no-till plow tillage Moebuis Clune 2008 Parana, Brazil no-till wheat-soybean no-till conventional tillage Moraes et al. 2016
Uttar Pradesh, India no-till rice no-till conventional tillage Naresh et al. 2014
Kpong, Ghana cover crop maize with stylosanthes guianesis, mucuna pruriens,
and mimosa invisa cover
crops
maize no cover crop Nyalemegbe et al. 2011
Harare, Zimbabwe crop rotation, no-till maize-sesbania and maize-A.
angustissima (crop rotation),
no-till
continuous maize (crop
rotation), conventional tillage
Nyamadzawo et al. 2003,
Nyamadzawo et al. 2008
Seville Province, Spain no-till wheat-sunflower no-till conventional tillage, reduced
tillage
Pelegrin et al. 1990
Multiple North America
locations: South Dakota,
North Dakota, Nebraska,
Saskatchewan
crop rotation, no-till maize-soybean-spring wheat-
alfalfa (crop rotation), maize-
soybean-sorghum-oat/clover
(crop rotation), spring wheat-
lentil (crop rotation), spring
wheat-pea no-till
continuous maize (crop
rotation x2 locations), spring
wheat only (crop rotation),
spring wheat-pea
conventional tillage
Pikul et al. 2005
Western Australia crop and livestock pasture grazed with sheep hayed pasture^ Proffitt et. al 1995
Punjab Province, India no-till soybean-wheat no-till conventional tillage Ram et al. 2013
Central Mozambique crop rotation maize-pigeonpea intercrop continuous maize Rusinamhodzi et al. 2012 Entre Rios Province,
Argentina
no-till wheat-maize-soybean no-till reduced tillage Sasal et al. 2006
Uttarakhand, India no-till rice-wheat no-till conventional tillage, reduced tillage*
Sharma et al. 2005
Uttarakhand, India cover crop maize-wheat with sunnhemp,
leucaena green manures
maize-wheat no cover crop Sharma et al. 2010
Jammu and Kashmir, India no-till maize-wheat no-till conventional tillage, reduced
tillage
Sharma et al. 2011
Alaska, USA no-till barley no-till conventional tillage, reduced tillage
Sharratt et al. 2006
Edmonton, Canada no-till continuous barley no-till conventional tillage Singh et al. 1996
Punjab Province, India cover crop rice-wheat with sesbania
aculeata green manure
rice-wheat without cover crop Singh et al. 2007
Uttar Pradesh, India no-till rice-maize no-till conventional tillage Singh et al. 2016
NSW, Australia no-till barley-oats no-till conventional tillage So et al. 2009
Hawkes Bay, New Zealand no-till, cover crop summer-winter vegetables (tomato, broad bean, sweet
maize, cauliflower, sweet
pepper, broccoli) with annual
ryegrass cover crop (cover
crop), no-till summer-winter
vegetables
conventional tillage, no cover crop
Springett et al. 1992
Maryland, USA cover crop maize with rye cover crop no cover crop Steele et al. 2012
Nkhotakota and Dowa
districts, Malawi
crop rotation, no-till maize-cassava-pigeon pea
(crop rotation), no-till
continuous maize (crop
rotation), conventional tillage
TerAvest et al. 2015
Central Greece cover crop cotton with vicia sativa or
durum wheat cover crop
no cover crop Terzoudi et al. 2007
Monze, Zambia crop rotation maize-cotton, maize-sunnhemp
continuous maize Theifelder and Wall 2010
Australia no-till sorghum-wheat no-till conventional tillage, reduced
tillage
Thorburn et al. 1992
Queensland, Australia crop rotation lucerne, medic annual pasture
and wheat#
continuous wheat Thomas et al. 2009
Uttarakhand, India no-till rice-wheat conventional tillage Tripathi et al. 2007 Punjab Province, India cover crop rice-wheat-Sesbania green
manure
no cover crop Walia et al. 2010
Shaanxi Province, China perennial alley cropping with walnut-wheat, monoculture walnut
continuous wheat Wang et al. 2015
Ibadan, Nigeria cover crop maize-cowpea-cassava with
cover crops
no cover crop Wilson and Lal 1982
Haryana, India no-till rice-wheat no-till conventional tillage Yaduvanshi and Sharma 2014
* Averaged controls # Experimental treatment confounded by livestock ~ He et al. et al. (2009) was confounded by the presence of residue retention in the experimental treatment; Liebig et al. (2004) was confounded by a second crop of winter wheat in the experimental treatment; and Bruce et al. (1992) was confounded by a different tillage system in the control (no-till plus a cover crop versus conventional tillage, no cover crop).
TABLE A.2. Description of Experiments Included in the Meta-Analysis Database: Changes in Grazing Management Complexity
* First
Author
Year
Pub. Site
Prec
(mm)
Live-
stock
Vege-
tation
Dur
(Y) Trt SR
(Orig)
AU/ha
d/
y
ha/A
U/y
(Trt)
AU/h
a
d/
y
ha/AU
/y
rest
(d)
%
red.
SR
Notes
Sharrow 2007 US,
OR
1085 S Pasture
(clover,
perennial ryegrass,
annual
grasses)
11 For ?
(M)
60.00 8 1 - - - - - 300-400
ewes/ha;
Apr, Jun; 4:60; res:5
cm
E Dedjir
Gamougou
n
1984 US,
NM
384 L Prairie
(shortgras
s prairie, grasses,
forbs)
12 R H 0.08 27
0
17 0.18 12
0
17.3 91 0 Rot (4-3)
Kumar 2012 US,
MO 967 C (beef,
520 kg) Pasture (tall
fescue,
red clover)
3 R M - 210
- - 35 - 17.5 0 Rot (6-paddock,
3 cattle)
E McGinty 1978 US,
TX
572 M (C,S,G;
3:1:1)
Woody
(mesquite,
threeawn,
sideoats)
7 R H 0.23 31
5
5 0.26 27
4
5.2 91 4 DR (4-3)
E Pluhar 1987 US,
TX
680 C (cow-
calf)
Prairie
(midgrass
, shortgrass
, native)
24 R M 0.20 31
5
5.8 0.30 27
4
5.8 91 0 DR (4-3)
Proffitt 1995 Australia
307 S Pasture (annual
legume
pasture-wheat)
1 Ada ? (M)
1.40 119
2.2 1.40 81 3.2 3 48 Removed occasional
ly based
on soil moisture
E Tadesse 2002 Ethiopi 1360 M (C,S,G) Perennial 4 R H 21.95 36 0.02 65.97 15 0.01 4 603 3d/wk
a (native
grasses, forbs)
5 6
Teague 2010 US,
TX
648 C (beef) Woody
(mesquite savanna,
grass &
forbs)
3 R M 0.12 22
0
14 0.95 28 14.0 68 0 Rot (8-1);
based on res
E Teague 2011 US,
TX
820 C (cow-
calf)
Prairie
(tall
grass)
9 R H 0.45 22
0
3.7 12.32 8 3.7 55 0 PMR
(based on
res) E Thurow 1986 US,
TX
609 M (C,S,G) Woody
(oak
mottes, bunchgra
ss,
sodgrass)
4 R H 0.33 24
0
4.6 4.46 18 4.6 50 0 SD (14-1;
4:50d)
E Weltz 1986 US,
NM
426 C Woody
(blue
grama, grasses,
forbs)
2 R H 0.07 36
5
13.5 - - 14.0 50 4 SD (4d
graze)
E " " " " " " 3 R M 0.04 365
26.6 - - 13.3 50 -50 SD (3d graze)
E Wood 1981 US,
TX
680 C (cow-
calf)
Woody
(wintergrass,
sideoats
grama)
4 R M 0.29 20
0
6.2 3.30 17 6.5 119 5 HILF; 8-
1; 17:119
E " " " " " " 20 R M 0.29 20
0
6.2 0.16 36
5
6.2 120 0 DR (4-3,
12:4m) Note: Studies that also had an exclosure treatment are indicated with an E in the leftmost column. Abbreviations used in this and following tables include: Livestock: C (cattle), M (mixed), S (sheep), G (goats), L (livestock); Dur (Y) = treatment duration in years; Trt = Grazing system treatment: C (continuous grazing), R (rotational grazing), Ada (adaptive grazing), For (agroforestry system); SR =stocking rate category: L (light), M(medium), H(heavy), if unclear, a “?” was added; “d/y” = number of days of grazing any given unit of land per year; rest (d) = number of days of rest of any given unit of land/year; % red. SR = the percent that stocking rates (ha/AU/y) were reduced as estimated by available data. While most studies noted that only complexity and not stocking rates were changed, there were a few exceptions. In the notes, specific grazing systems were noted if mentioned clearly by the authors: HILF: High intensity low frequency, DR: Deferred rotation, SD: Short duration, PMR: Planned multipaddock rotational, Rot: Rotational, Res: Residual biomass.
TABLE A.3. Description of Experiments Included in the Meta-Analysis Database: Changes in Grazing Rates or Pressure
First
Author Year Site
Prec
(mm)
Live-
stock Vegetation
Dur
(Y) Sys
SR
(Orig)
SR
(Trt)
(Orig)
AU/ha
d/
y
ha/AU/
y
Variable
changed V0 V1 V2 V3 Notes
E Bari 1993 Pakist
an
625 L Grass
(grasses,
forbs)
2 C H M,L - - - Res
phytomass
(kg/ha)
624 65 131 - 300-400
ewes/ha;
Apr, Jun; 4:60; res:5
cm
Chartier 2011 Argentina
258 S Woody (grass to
shrub
steppe; perennial
grasses)
- C H M,L 0.1 365
16.7 Veg Grass stepp
e
Grass stepp
e
Shrub
step
pe
- Rot (4-3)
E Dedjir
Gamougoun
1984 US,
NM
384 L Prairie
(shortgrass
prairie, grasses,
forbs)
3 C H M - - 17.3 ha/AU 17 25 - - Rot (6-
paddock, 3
cattle)
E du Toit 2009 S Africa
366 S Woody (common
shrubs,
karoo bushes,
grasses)
2 C H M,L 1.8 30 6.8 SSU/ha 16 50 75 - DR (4-3)
E Franzluebbers
2011 US, GA
1250 C (yearl. steers)
Pasture (Bermuda
grass, tall
fescue; hayed 1/mo
to 5cm
12 C H L 4.1 270
0.3 steer/ha 9 33 - - DR (4-3)
E Mwendera 1997 Ethiopia
1000 C (cows, oxen)
Perennial (native
grasses)
1 C V L,M,H
- 365
0.8 AUM/ha 4 29 57 86 Removed occasionall
y based on
soil moisture
Note: The “variable changed” as reported by the authors is listed in the table, and the original value (V0) of that variable is noted as well as the percent reduction (V1, V2, V2, represent the value that the given variable decreased by as calculated from reported data
E Pluhar 1987 US,
TX
680 C (cow-
calf)
Prairie
(midgrass, shortgrass,
native
range)
1 R V H 12.5 8 3.6 ha/cow/y 13 66 - - 3d/wk
E Savodogo 2007 Burki
na
Faso
841 M (C, S,
G, wild)
Woody
(savanna,
annual/perennial grass)
1 R V L,M,
H
0.2 40 45.6 280kg/d/h
a
8 25 50 75 Rot (8-1);
based on
res
E Taddese (b) 2002 Ethio
pia
1000 C (cow,
oxen)
Perennial
(native grasses)
1 C V L,M,
H
- 36
5
3.4 AUM/ha 4 29 57 86 PMR
(based on res)
E Tadesse 2003 Ethio
pia
1095 C (cow) Perennial
(native grasses,
forbs)
2 C H M - 36
5
3.4 AUM/ha 4 57 - - SD (14-1;
4:50d)
E Teague 2011 US,
TX
820 C (cow-
calf)
Prairie (tall
grass
prairie)
9 C H L 0.4 22
0
3.7 AU/100ha 27 48 - - SD (4d
graze)
E Thurow 1986 US, TX
609 M (C, G, S)
Woody (oak mottes,
bunchgrass,
sodgrass)
6 C H M 0.3 240
4.6 ha/au/y 5 43 - - SD (3d graze)
Warren (a) 1986 US,
TX
609 M (C,G,S;
1.63:1:1)
Woody (live
oak, grass,
savanna)
2 R H M,L 2.9 26 4.8 ha/AU 0.3 37 53 - HILF; 8-1;
17:119
E Warren (b) 1986 US,
TX
609 C (heifers) Bare
(herbicide +
drought killed forbs)
1 R V M,H 6.8 20 2.7 ha/AU/y 2.7 34 67 - DR (4-3,
12:4m)
E Weltz 1986 US,
NM
426 C Woody
(blue grama,
grasses,
forbs, etc.)
18 C H M 0.1 36
5
13.5 ha/AU 14 25 - -
E Wood 1981 US, TX
680 C(cow-calf)
Woody (winter
grass,
sideoats grama,
mesquite)
20 C H M 0.2 365
4.6 ha/AU 5 25 -
E Zhou 2010 China 505 M (G,S,
4:1)
Grass 13 C H M 0.2 36
5
- trampling H M - - trampled
path vs.
pasture
and in order of increasing degree of change.) Abbreviations are as noted above.
TABLE A.4. Description of Experiments Included in the Meta-Analysis Database: Exclosure Experiments (not included in A.3. or A.4.)
First
Author Year Site
Prec
(mm) Livestock Vegetation
Dur
(Y) Sys
SR
(Orig) AU/ha d/y ha/AU/y
Grazing
Notes Excl. Notes
Achouri 1984 US, UT 250 C Perennial (crested wheatgrass)
20 C M - 90 4.5 M (1.5 ha/AUM) for
several y (Jun-
Aug)
ungrazed for >20 y
Allington 2011 US, AZ 395 C Perennial (hairy
grama, grasses,
shrubs)
40 R M (?) 0.1 7 - SDRG
(<1wk); avg
of 1AU/13ha
Research ranch (ungrazed),
across fence
Bharati 2002 US, IA 851 C Pasture (grass,
brome, timothy)
6 C - - - - "C grazed
pasture"
"Grass filter" (ungrazed area)
Busby 1981 US, UT 345 C Perennial (crested wheatgrass,
deforested pinyon-
juniper)
5,1 R? M - 75 - "M to H" May1-Jun15
& Oct1-Nov1;
3 trt
Ex in each trt
Castellano 2007 US, AZ 350 L Shrub/Desert
(acacia, etc.)
52, 25,
10
C - - - - Open grz since
late 1800s
3 ex: 1997(20ha), 1993 (1ha),
1958 (9.3ha)
Gifford 1982 US, ID 305 C Perennial (crested wheatgrass, grass;
rep big sagebrush)
1,2,4,6 C - - 120 - Seasonal 3 30x30m ex installed
Jeddi 2010 Tunisia 196 L Steppe (arid, degraded)
6,12 C - - - - C grazed area Ex set up gradually by Sfax FS
Kato 2009 Mongolia 181 M(S,G,C,H) Grass steppe
(perennial grass, forbs, tallgrass)
4 C V - 365 - "long been
subject to intensive
grazing"
1.5m fence
" " " 213 " Grass steppe 4 C H - 365 - "L #'s have increased
considerably"
1.5m fence
" " " 162 " Shrub/Desert
(acacia, etc.)
4 C M - 365 - Airport grounds; trt likely >4y
but not reported Kauffman 2004 US, OR 320 C Meadow (dry &
wet, herb. riparian
plants, grass, sedge)
7 C M (?) - 75 - 1 site: deferred
grz, summer;
2 sites: July1-Sept15);
Avg of ex at each (19,7,7),
accidental and wild grazing has
occurred; wet, dry meadows measured separately at each of
3 sites
Krzic 1999 BC 355 C(Cow-
Calf)
Pasture (lodgepole
pine plantations)
8 C M (?) - 30 - Grz to 50%
forage use for
1 summer mo;
2 0.5ha ex (1 for each of 2
seeding trt); protection from
new grazing (not grazed previously).
Lavado 1994 Argentina 950 C(Cow-
Calf)
Perennial (Natural
vegetation, grasses)
3, 12 C H 1.4 365 0.7 Reported in
AU/ha/y; "C grz in a H SR"
2 2-ha enclosures of different
ages (3, 12 y)
Takar 1990 Somalia 446 M(C,G) Grass (shrubs,
annual grass/forbs)
3 C H - 365 5 "grazed
heavily w/C&G by
seminomadic pastoralists"
2-ha livestock exclosure
Tukel 1984 Turkey 362 L Grass (steppe,
forage grass, shrubs)
30 C H - 365 - "heavy
grazing on public range"
protected area
Tromble 1974 US, AZ 312 M(C,G,S) Grass (black
grama, fmesquite,annuals)
9 - - - - - "grazed" "ungrazed site had been
protected from livestock use for the past 9 y"
Wheeler 2002 US, CO 407.7 C (Steers) Riparian (willows,
sedge)
39 C H 20.4 5 - 1x H grz
(6/0.25 ha) on protected
paddocks; Grz
to 60-75% use; avg
spring/summer
grz
3 ungrazed paddocks/trt
Note: All exclosure studies that were not represented in either of the first two appendices (i.e., studies that did not include a treatment representing increased grazing land management complexity or a reduction in stocking rates or pressure).
Appendix B: Methods and Experiments included in the Porosity
and Field Capacity Meta-Analysis2
Database Development
The goal of this analysis was to understand the impact of continuous living cover on soil hydrologic properties in agricultural
systems using a meta-analysis approach. Therefore, the first step was to develop a database of studies that could be included in the analysis.
The two major criteria for database inclusion were (1) studies compared land managed with continuous plant growth (including cases of
actively restored perennial landscapes) versus annual crop systems that did not include continuous plant cover; and (2) studies measured at
least one of two indicators of soil hydrology: water retained at field capacity (the maximum level of plant-available soil water, hereafter
referred to as field capacity) or total porosity (the maximum volume of water that soil can hold). Several different treatment practices
representing continuous living cover were sought for inclusion in the database:
1. Cover crops, where a cover crop was grown in between the harvest of annual cash crops (compared to leaving soil uncovered
in the control treatment)
2. Perennial grasses, including grazing systems with either native or cultivated grasses, Conservation Research Program (CRP)
protected conservation lands, perennial bioenergy, or forage crops
3. Agroforestry systems
4. Managed forestry systems
The EBSCO Discovery ServiceTM was the primary search engine used to compile the database for this analysis. It searches a
comprehensive collection of titles, including more than 23,000 publications from databases such as JSTOR and publishers such as Wiley,
Elsevier, Springer-Nature, IOP, Royal Society, Oxford, Cambridge, Thomson Reuters, AAAS, and the American Society of Agronomy. The
EBSCO Discovery ServiceTM matches on subject headings, keywords, and abstracts, making it an ideal search engine for building a database
targeted to the highly specific question in this analysis. The keyword search included descriptors of the soil properties (given the multiple
terms that might be used to describe field capacity) as well as the different continuous living cover practices. The search terms included were:
water retention OR field capacity OR moisture retention OR porosity AND perennial W1 grass* OR cover crop* OR agroforest* OR forest*.
These keyword terms found > 400 studies, of which 25 ultimately fit our criteria.
To supplement the EBSCO Discovery ServiceTM search, the USDA-NRCS Soil Health Literature Database (NRCS, 2016) was used
to find additional research papers. This database is an ongoing effort of the NRCS Soil Health Division to categorize the impact of
conservation practices on soil properties and uses large search databases (including Google Scholar) to find papers. It is updated regularly by
staff and currently includes more than 300 peer-reviewed references. The database allows users to search specific soil properties, including
water retention and soil porosity, as well as specific treatments based on established NRCS practice codes. From this search, we added two
additional studies, for a total of 27 studies representing 93 separate paired observations for both soil properties analyzed. Only three studies
included field measurements of both variables.
Several studies had complex treatment or control scenarios and were entered into the database only after careful consideration.
Some experimental designs (i.e., with a variety of cover crop or perennial grass treatments) allowed for multiple comparisons to be created
within individual experiments. If an experiment included multiple treatments that could be considered a control (i.e., different annual
cropping systems, see Tables 1 and 2), these were averaged to represent one control treatment. Also, for some of the most complex studies, it
was not possible to develop comparisons between treatments that solely tested the isolated effect of the continuous living cover treatment to
an annual cropping system control. For example, several experiments included perennial grasses with livestock grazing compared to annual
crops, such that the inclusion of grazing animals was a confounding factor. While not ideal, these studies were maintained in the database as
they still represented important differences between annual and perennial based systems.
2 Adapted from Basche and DeLonge (n.d.)
Steps were taken to ensure that field measurements were extracted from each paper as consistently as possible. For example, for the
field capacity measurements, if authors described a specific potential pressure typical for their location, then this was the potential pressure
that was utilized for the database. When experiments did not assign a specific potential pressure associated with field capacity, potentials in
the range of -10 kPa to -33 kPa were selected, and if multiple measurements in this range were reported, they were averaged (Hillel 1998; see
Table 2). This analysis specifically focused on the wetter range of the water retention curve because the pore sizes that affect this range are
the ones understood to be affected by management (Kay 1998). For porosity, only studies that included measurements for total porosity, as
opposed to measurements of only macro-, micro-, or porosities associated with different particle and aggregate sizes, were included in the
database. This was done in an attempt to keep the comparison as standardized as possible across the range of soil textures. If experiments
measured properties more than once in a season or for multiple depths, these measurements were averaged to create one comparison per
treatment. Several studies reported measurements that were taken at the end of a season for multiple years and these were counted as separate
paired observations.
Statistical Analysis
Response ratios were calculated as the ratio of the soil water property measured in areas with continuous living cover treatments as
compared in annual cropping system controls. The natural log of the response ratio was calculated for the two soil properties separately and
used as the basis for all statistical analyses (Equation 1) (Hedges et al. 1999). For meta-analysis, a weighting factor is typically developed to
give more weight to studies with greater levels of precision or lower within-study variability (Philibert 2012). As many of the experiments in
this database did not provide measurements of within-study variability (standard deviations or standard errors), the number of experimental
replications were used as an alternative method to develop a weighting factor (Equation 2) (Adams et al. 1997). In studies with experimental
designs that did not include true replication (i.e., relying instead on multiple subsamples from different treatments), a replication size of “1”
was assigned to create a lesser weight for those experiments in the calculation of mean effect sizes (Tables 1 and 2).
The primary statistical analysis was conducted using R (Version 1.0.136, R Core Team, 2009-2016). A mixed effects model (lmer4
package) was used to calculate mean effects, including a random effect of study and the weighting factor of experimental replications. The
random effect of study is similar to a “block” effect, accounting for similarities in environments when more than one response ratio was
available for one study (Eldridge et al. 2016; St-Pierre 2001). In addition to calculating overall mean effects of treatments for each soil water
property, studies were analyzed in groups according to soil texture, annual precipitation, or the inclusion versus exclusion of livestock; for the
statistical analysis, these groups were treated as fixed effects. If 95 percent confidence interval did not cross zero, results were considered
significant. For ease of interpretation, the log response ratios were back transformed and converted to percentages (Equation 3).
LRR = ln ( Experimental Treatment X
Control Treatment X) (1)
Where X is either porosity or field capacity
Wi = Experimental Reps * Control Reps
Experimental Reps + Control Reps (2)
Percent change =[Exp(LRR) - 1] * 100 (3)
TABLE B.1. Experiments Measuring Total Porosity in the Meta-Analysis Database
Location Treatment Category Control Treatment Experimental
Design Reference
Denmark Cover crop Spring barley With radish cover crop Split plot, 3
replications
Abdollahi and
Munkholm al. 2014
Nigeria Perennial grass Cereal-legume continuous cropping
Perennial pasture grasses with 2 months controlled
grazing
5 adjacent ~2.5 ha field sites, sampled 9
locations from each
site
Abu 2013
France Cover crop Barley, pea, and wheat
without cover crops
With legume cover
crops, managed as living
mulches
Sampled from 6
locations in each
treatment
Carof et al. 2007
Italy Perennial grass Continuous wheat Perennial pasture 2 replications Chisci et al. 2001
Brazil Cover crop Fallow, ruzigrass, sorghum
With sorghum-sudangrass, sunhemmp,
millet cover crops
Randomized complete block, 4
replications
Garcia et al. 2013
Iran Perennial grass Continuous wheat Pasture with livestock Sampled from 6
points in each land
use
Haghighi, Gorji and
Shorafa 2010
Ethiopia Agroforestry Maize-based conventional tillage
Agroforestry based conservation with
livestock
Sampled from 4 areas in two adjacent
fields
Ketema and Yimer 2014
China Perennial grass Annual oats Perennial pasture with livestock grazing
3 replications Li et al. 2007
Pakistan Cover crop Cotton-wheat Berseem green manure 4 replications Mahmood-ul-Hassan,
Rafique and Rashid 2013 Victoria, Australia Perennial grass,
agroforestry
Continuous annual
cropping
Perennial pasture & alley
cropping
2 replications of
pasture, 3
replications of alley cropping and
continuous annual
cropping
Mele et al. 2003
Ontario, Canada Cover crop Continuous corn Corn, corn, oats, barley
with red clover cover
crop
Randomized split
plot, 4 replications
Munkholm, Heck and
Deen 2013
Ghana Cover crop Maize-fallow With mucuna,
stylosanthes and mimosa
cover crops
Split plot, 4
replications
Nyalemegbe et al. 2011
North Carolina Perennial grass, forestry Conventionally tilled
corn, peanuts, cotton,
soybeans
Integrated livestock and
pasture, black walnut
plantation forestry woodlot
3 replicated blocks
(8-ha each) with five
subplots for different treatments
Raczkowski et al. 2012
Argentina Perennial grass Average of corn and
soybean treatments
Pasture Sampled from 5
locations in each treatment
Sasal et al. 2010
Brazil Agroforestry Corn-soybean Silvopasture, agro-
silvopasture with livestock
Adjacent fields,
sampled from four transects per field
Silva et al. 2011
Illinois, USA Cover crop Corn-soybean With rye, vetch, rye +
vetch cover crop
Randomized
complete block, 4 replications
Villamil et al. 2006
TABLE B.2. Experiments Measuring the Water Retained at Field Capacity in the Meta-Analysis Database
Location Treatment
Category Control Treatment
Experimental
Design
Pressure
Reported for
Volumetric
Water Content
Used in LRR
Reference
Nigeria Perennial grass Cereal-legume
continuous cropping
Perennial pasture
grasses with two months controlled
grazing
5 adjacent ~2.5
ha field sites, sampled nine
locations from
each site
Assigned -10 kPa
as field capacity
Abu 2013
Iowa, USA Cover crop Corn-soybean With rye cover crop Randomized
complete
block, 4 replications
Assigned -33 kPa
as field capacity
Basche et al. 2016
Missouri, USA Perennial grass Corn-soybean (average
of till and no till treatments)
Timothy grass and
restored prairie
Sampled from
6 replications in adjacent
fields
Reported -10 kPa, -
20 kPa, -33 kPa, averaged values
Chandosoma et al.
2016
Missouri, USA Cover crop, perennial grass
Mulch-till corn-soybean
No-till corn-soybean-wheat with red
clover, CRP, pasture
Randomized complete
block, 3
replications
Reported -10 kPa, -20 kPa, -33 kPa,
averaged values
Jiang et al. 2007
Tennessee, USA Cover crop Cotton With rye-vetch cover
crop
4 replications Reported -10 kPa, -
15 kPa, -20 kPa, -30 kPa, averaged
values
Kiesling et al.1994
Georgia, USA Forestry Corn-soybean conventional tillage
Long leaf pine, planted pine
Randomized complete
block, 3
replications
Assigned -10 kPa as field capacity
Levi et al. 2010
Zimbabwe Agroforestry Continuous maize Improved fallow w/
acacia & sesbania
Randomized
complete
block, 3 replications
Reported
volumetric water
content between -5 kPa & -33 kPa
Nyamdzawo et al.
2012
Louisiana, USA Cover crop Cotton With common vetch
or hairy vetch cover crops
3 replications Assigned 1/3 atm
as field capacity
Patrick et al. 1957
North Carolina Perennial grass,
forestry
Corn, peanuts, cotton,
soybeans (average of
till and no till
treatments)
Integrated livestock
and pasture, black
walnut plantation
forestry woodlot
3 replicated
blocks (8-ha
each) with five
sub-plots for
different
treatments
Assigned -10 kPa
as field capacity
Raczkowski et al.
2012
Texas, USA Perennial grass,
cover crop
Sorghum-wheat
conventional tillage
CRP, grazed
grassland
Sampled 3
different
locations according to
soil type in
adjacent fields
Reported -10 kPa, -
30 kPa, averaged
values
Schwarz et al. 2003
Brazil Agroforestry Corn-soybean Silvopasture, agro-
silvopasture with
livestock
Adjacent
fields, sampled
from four transects per
Assigned 0.01 MPa
as field capacity
Silva et al. 2011
field
India Cover crop Rice-wheat With sesbania green manure
Randomized complete
block, 3
replications
Assigned 0.3 bars as field capacity
Walia et al. 2010
Nigeria Cover crop Maize-cassava-cowpea With cover crops Randomized
complete
block, 3
replications
Assigned pF 2.5 as
field capacity
Wilson and Lal 1982
China Forestry Wheat, rapeseed,
canola
Afforestation 5 samples
taken from
adjacent fields
Assigned pF 2.5 as
field capacity
Yu et al. 2015
Location Treatment Category Control Treatment Experimental
Design
Pressure Reported
for Volumetric
Water Content Used in LRR
Reference
Nigeria Perennial grass Cereal-legume
continuous cropping
Perennial pasture
grasses with two months controlled
grazing
5 adjacent ~2.5
ha field sites, sampled nine
locations from
each site
Assigned -10 kPa
as field capacity
Abu 2013
Appendix C: Methods for the Hydrology Modeling Analysis3
Methods
The Basin Characterization Model (BCM) is a grid-based hydrology platform that calculates water balance and has been utilized
extensively across the western United States to evaluate hydrologic response to changes in climate (Thorne et al. 2015; Flint et al. 2013; Flint
and Flint 2008). Prior applications of the BCM have evaluated how soil improvements through rangeland management alter the hydrologic
balance in California. A goal of this analysis was to similarly analyze how soil improvements through agricultural management lead to
landscape hydrologic impacts; because the soil profile properties in the BCM represent the central reservoir for water storage and runoff, it
was a well-suited tool for this analysis.
We ran the BCM at a monthly time step with a 250-m grid cell size applied to 17 watersheds in Iowa (Figure 1; Table 1). These
watersheds were selected to represent the various ecological and climatological regions covering a large geographic extent of the state and to
capture watersheds that include or flow into major urban areas. Datasets were developed to reflect the climate (precipitation, temperature, and
potential evapotranspiration), soils, geology, land cover, and elevation of Iowa (Table 2). Potential evapotranspiration input data was
generated first for clear sky conditions with a solar radiation model that used the Priestley-Taylor equation and incorporated state specific
parameters of slope, aspect, and topography. Cloudiness corrections were made using data for 16 stations from the Iowa Environmental
Mesonet (IEM 2016; Flint et al. 2013). Soil texture and organic matter data from the Soil Survey Geographic Database (SSURGO; Soil
Survey Staff 2016) were used to calculate soil hydraulic properties using the pedotransfer functions outlined in Saxton and Rawls (2006)
(Table 2). Values for the permanent wilting point and field capacity were selected based on agricultural soil convention, which is known to
vary between locations (1.5 MPa and 0.033 MPa were chosen, respectively; see Hillel 1998). For this BCM application, adjustments were
made to explicitly incorporate crop water use. This required a closer estimation of the plant rooting zone, which was then limited in regions
of maize and soybean assuming an average rooting depth of 0.8-1m. These crops represent 94 percent of harvested cropland in the state
(USDA-NASS 2014).
An iterative calibration was conducted using two main sources of data: (1) a unique dataset created by the United States Geological
Survey (USGS) of 1-km2 evapotranspiration data for the contiguous United States calibrated to several remote sensing products and
constrained by water balance calculations (Reitz et al. 2015); and (2) USGS stream flow data for each of the 17 watersheds. Information from
additional station locations was sought for watersheds that required addition or subtraction of water flow into station locations. Initial crop
and land use k-factors were selected in accordance with the Food and Agriculture Organization (FAO) crop water use guidelines (FAO 1992)
and then iteratively adjusted to better reflect stream flow as well as monthly evapotranspiration estimates (Table 3), where actual
evapotranspiration was divided by potential evapotranspiration and spatially extracted for individual vegetation types. Bedrock permeability
values were also altered to best match stream flow as a proxy for the predominantly tile drained landscape of this region.
Recharge and runoff predicted by the BCM was used with postprocessing equations (see below) to calculate basin discharge for 17
basins and matched to measured hydrographs as described by Flint et al. (2013). Goodness-of-fit statistics included percent bias (PBIAS)
values for the 17 basins, ranging from -4.8 to 0.4 percent, and Nash-Sutcliffe Efficiency (NSE) values ranging from 0.16 to 0.78, with an
average of 0.55. Moriasi et al. (2007) propose that PBIAS values that are ±25 percent, and all of the basins fell within this range. Further,
NSE values > 0.50 are thought to represent satisfactory performance of monthly stream flow predictions (Moriasi et al. 2007). Given that the
predominant land use in Iowa is agricultural, and the landscape includes extensive tile drainage, we considered these values to be suitable for
our analysis after careful consideration of hydrographs that matched periods of peak flow well.
A series of additional model scenarios were established that evaluated agricultural land use change, subsequent soil improvements,
and hydrologic change for historical and future projections of climate (Table 2; Table 4). Given prior research that predicted reduced flood
frequency and intensity with more perennial vegetation (Schilling et al. 2014), we sought to understand how, in addition to crop water use,
soil hydrologic improvements play a role in these impacts. Further, a global meta-analysis recently found that agricultural management that
includes “continuous living cover” (i.e., cover crops, perennials crops, and agroforestry) increases total porosity and field capacity by an
3 Adapted from Basche et al. (n.d.)
average of 8 to 9 percent compared to annual crop systems (Basche and DeLonge n.d.). These are two important soil hydrologic inputs to the
BCM and served as the basis for the land use change scenarios outlined in Table 4. Two other modeling analyses for Iowa, which evaluated
the vulnerable and less productive landscape regions, were utilized to evaluate in a geographic fashion where perennial landscapes would be
most effectively targeted: (1) the Daily Erosion Project (Cruse et al. 2006), which is an ongoing effort by midwestern scientists to predict at a
HUC12 scale the extent of soil erosion using the Water Erosion Prediction Project (WEPP) model, to determine the most erodible regions in
the state; and (2) a subfield profitability analysis as described by Brandes et al. (2016) and updated for 2012 to 2015, in which soil
characteristics, average crop yields, production costs, and commodity prices were integrated at a subfield resolution to determine regions of
the state that were more or less profitable on an annual basis.
We evaluated the National Weather Service “flood stage” values for specific locations that corresponded to our modeled domain.
Flood stage is defined as “the stage at which overflow of the natural banks of a stream begin to cause damage in the local area from
inundation (flooding)” (USGS 2017a). Flood stage values are equated to a stream flow value by USGS that we used to estimate the number
of months that experienced water flows above a particular location’s flood stage (USGS 2017b). We then calculated how many of those
months had lower flow values in our modeled predicted stream flow compared to the baseline land use and the shifts in most erodible lands
scenarios.
The procedure for calculating basin discharge values was as follows (see Flint et al. 2013 for a more thorough review of the
postprocessing equations): To compare predictions to measured stream flow data, all grid cells within each basin domain are summed based
on the individual grid-cell values of monthly predictions for runoff and recharge. Further, the water balance is conceptualized into three
connected groundwater reservoirs: (1) the surface reservoir, representing runoff and seepage; (2) the shallow groundwater reservoir,
representing the shallow transient saturated zone that seasonally provides much of the base flow but can be event driven; and (3) deep
groundwater reservoir representing any regional aquifer processes and can contribute to the shallow groundwater reservoir.
A series of equations in successive time steps (i) partitions water to represent the three reservoirs, based on the BCM predictions of
runoff (BCMrun) and recharge (BCMrch).
The surface reservoir:
[1] GWsurface(i) = GWsurface(i-1) + BCMrun(i) – Surfaceflow(i-1)
Where Surfaceflowi is:
[2] (SurfaceScaler * GWsurface(i))SurfaceExp
SurfaceScaler and SurfaceExp represent coefficients to match peak and recessional flows and are typically ≤ 1.
The shallow groundwater reservoir:
[3] GWshallow(i) = GWshallow(i-1) + BCMrch(i) – shallowflow(i) – deepflow(i)
Shallowflow(i) is:
[4] (ShallowScaler * GWshallow(i-1))ShallowExp
ShallowScaler and ShallowExp represent coefficients to match base flow that are ≤ 1.
The deep groundwater reservoir:
[5] Deepflow(i) = (DeepScaler * GWshallow(i-1))DeepExp
This reservoir is subtracted from the shallow reservoir to simulate deep groundwater recharge. DeepScaler and DeepExp are
coefficients that are ≤ 1 used to maintain a mass balance of water flow by limiting shallow groundwater entering stream flow.
Stream flow upstream of the observation gage is calculated as the sum of the surface and shallow reservoirs.
[6] Stream(i) = GWsurface(i) + GWshallow(i)
Basin discharge:
[7] Discharge(i) = AquiferRch * Stream(i)
AquiferRch is a coefficient used to account for impairment to flows where basins gain (>1) or lose flow (<1) in the long term. BCM
predictions of runoff and recharge represent hydrologic conditions that are assumed free of additional processes such as diversions, reservoir
storage, urban runoff, or groundwater pumping. These assumptions could further account for errors between measured stream flows in the
modeled domains. Approximately 30 to 40 percent of harvested cropland in Iowa includes subsurface tile drainage (USDA 2014; Sugg
2007), which can be considered an additional process unaccounted for by explicit model representations. As a result, aquifer recharge values
were generally lower than 1 in our post-processing equations (average of 1.03).
TABLE C.1. Stream Gauges and Watersheds Used in BCM Simulations in Iowa, Discharge Equation Coefficients and
Goodness of Fit Statistics
Station Name NWIS
Station
Su
rfa
ceE
xp
Sh
all
ow
Sca
le
Sh
all
ow
Ex
p
Dee
pS
cale
Dee
pE
xp
Aq
uif
erR
ch
NSE PBIAS
Fort Dodge 5480500 0.99 1 0.99 1 0.85 1.06 0.54 -0.81
Cedar Rapids 5464500 0.99 1 0.95 1 0.92 0.96 0.47 -0.28
Omaha 6610000 0.99 1 0.9 1 0.65 0.91 0.51 -0.38
Independence 5421000 0.99 1 0.97 1 0.88 0.96 0.65 0.21
Van Meter 5484500 0.99 1 0.94 1 0.88 1.02 0.59 -0.27
Sigourney 5472500 0.98 1 0.9 1 0.95 1.1 0.59 -4.84
Randolph 6808500 0.98 1 0.85 1 0.97 0.98 0.78 -0.60
Ottumwa 5489500 0.99 1 0.94 1 0.78 1.06 0.16 0.11
Red Oak 6809500 0.99 1 0.94 1 0.95 0.93 0.72 0.29
Clarinda 6817000 0.99 1 0.9 1 0.95 0.79 0.70 -0.70
Rowan 5449500 0.99 1 0.97 1 0.91 1.03 0.48 -0.03
Ames 5471000 0.99 1 0.97 1 0.93 1.02 0.42 -0.20
Marengo 5453100 0.99 1 0.87 1 0.94 1.32 0.50 0.38
Wapello 5465500 0.97 1 0.84 1 0.94 1.09 0.33 -0.02
Garber 5412500 0.99 1 0.88 1 0.88 0.93 0.70 -0.53
Maquoketa 5418500 0.97 1 0.8 1 0.9 0.94 0.61 -0.07
Dewitt 5422000 0.96 1 0.85 1 0.88 1.4 0.54 -0.44
TABLE C.2. Crop Coefficients Used for Various Crop and Land Uses
Data Source Reference
Soil Soil texture and % organic matter (to generate the upper and lower end of
plant available water, and total porosity)
SSURGO SSURGO, Saxton
and Rawls 2006
Climate Precipitation, temperature (Tmax, Tmin), potential evapotranspiration PRISM, Iowa Environmental
Mesonet IEM 2016
Climate Future climate change (RCP 8.5) CMIP5 CMIP5 2016
DEM Digital elevation map USGS USGS 2015
Geology Geology USGS USGS 2005
Land Use 2016 cropland data layer USDA USDA-NASS 2017
Additional
Scenarios
Erodible land Cruse et al. 2006 Subfield Profitability
Analysis
Daily Erosion Project Regions of greater and lesser profitability
Brandes et al. 2016
FIGURE C.1. Geographic Extent of Modeling and Watershed
Boundaries
TABLE C.3. Crop Coefficients Used for Various Crop and Land Uses
Corn Soybean Pasture Alfalfa Forest Water Urban Wetland
Oct 0.20 0.20 0.13 0.13 0.12 0.18 0.04 0.18
Nov 0.13 0.12 0.11 0.12 0.11 0.19 0.05 0.19 Dec 0.05 0.05 0.12 0.13 0.10 0.12 0.11 0.12
Jan 0.05 0.05 0.09 0.11 0.15 0.05 0.10 0.05
Feb 0.05 0.05 0.09 0.10 0.14 0.06 0.08 0.06 Mar 0.05 0.05 0.11 0.11 0.14 0.08 0.12 0.08
Apr 0.20 0.20 0.11 0.11 0.32 0.15 0.14 0.15
May 0.25 0.25 0.27 0.28 0.32 0.25 0.17 0.25 Jun 0.50 0.50 0.27 0.27 0.36 0.29 0.19 0.29
Jul 1.03 1.03 0.39 0.39 0.42 0.39 0.19 0.39
Aug 1.06 1.06 0.49 0.49 0.48 0.40 0.16 0.40 Sep 0.50 0.50 0.37 0.37 0.39 0.32 0.09 0.32
TABLE C.4. Land Use Change Scenarios Evaluated in the Analysis
* Kfactors for additional perennial plants were based on the calibrated pasture kfactors (C.3.) and from FAO values for pasture grass (1992). For corn or soy with a cover crop, the summer month (June to September) used the kfactors for corn or soybean, while for the remaining months pasture kfactors were used (minus May when it was lowered slightly to better represent cover crop termination before cash crop planting) (FAO 1992). ^ Future climate included analysis of three different global climate models using the representative carbon pathway 8.5: Canadian Centre for Climate Modeling and Analysis (CanESM2), Japan Agency for Marine-Earth Science and Technology (MIROC-ESM), and the Met Office Hadley Center (HadGEM2-ES). These were selected based on global average temperature and precipitation changes predicting a range of wetter, drier, hotter, and cooler average changes by the end of the 21st century. For the locations selected in this analysis, the three GCMs predicted an average increase in rainfall of 4.9 percent and a maximum temperature increase of 7 to 9ºC for the 2070 to 2099 period.
Scenario Changes Timeframe
Baseline Current land use and soil conditions Historic: 1981–2015, Future: 2070–2099^
EROD Perennial crops* on all cropland with >5 tons acre-1 erosion rates, corn or soybean with a cover crop* on cropland with 2–5 tons acre-1 erosion rates,
land converted has 8–9% improvement in field capacity and porosity
Historic: 1981–2015, Future: 2070–2099^
PROF Perennial crops on cropland that is the least profitable regions (mean profitability 2012–2015 below $-82 ha-1), corn or soybean with a cover crop
on the next least profitable regions ($-82 to $56 ha-1), land converted has 8–
9% improvement in field capacity, porosity
Historic: 1981–2015, Future: 2070–2099^
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